Uploaded May 2025 | Updated September 2026, 2 weeks ago
Watch how I built an AI-powered bird feeder that detects birds in real time using a custom-trained YOLOv7-Tiny model running directly on the AMB82-MINI!
This video walks you through the entire process — from dataset preparation to deploying your own .nb model on the device using Arduino.
Highlights:
• Download & filter bird dataset
• Train YOLOv7-Tiny (PyTorch)
• Reparameterize + convert to Realtek .nb
• Run it standalone on the AMB82-MINI
No cloud, no server — just a camera, a model, and some curious birds.
Hardware:
• Realtek AMB82-MINI
• SD Card
• (Optional) Bird feeder or enclosure
Tools Used:
• PyTorch + YOLOv7
• Realtek AI Model Converter
• Arduino IDE
Please refer to the project GitHub for the script used here.
github.com/0015/yolov7/tree/AMB82-Mini_Bird_Detector/projects/AMB82-Mini_Bird_Detector
Realtek AMB82-Mini IoT AI Camera Arduino Dev. Board
amzn.to/4dtBvFS
Home Depot Kids Workshop
homedepot.com/c/kids-workshop
Watch how I built an AI-powered bird feeder that detects birds in real time using a custom-trained YOLOv7-Tiny model running directly on the AMB82-MINI!
This video walks you through the entire process — from dataset preparation to deploying your own .nb model on the device using Arduino.
Highlights:
• Download & filter bird dataset
• Train YOLOv7-Tiny (PyTorch)
• Reparameterize + convert to Realtek .nb
• Run it standalone on the AMB82-MINI
No cloud, no server — just a camera, a model, and some curious birds.
Hardware:
• Realtek AMB82-MINI
• SD Card
• (Optional) Bird feeder or enclosure
Tools Used:
• PyTorch + YOLOv7
• Realtek AI Model Converter
• Arduino IDE
Please refer to the project GitHub for the script used here.
github.com/0015/yolov7/tree/AMB82-Mini_Bird_Detector/projects/AMB82-Mini_Bird_Detector
Realtek AMB82-Mini IoT AI Camera Arduino Dev. Board
amzn.to/4dtBvFS
Home Depot Kids Workshop
homedepot.com/c/kids-workshop
![Who is Secretly Talking on Your Wi-Fi Right Now?
Ever wondered what’s floating through the air right now? Built a completely standalone, passive Wi-Fi traffic analyzer using Raspberry Pi Zero 2 W! No network connection, zero active pings—just stealth listening to invisible raw packets.
[Uninterruptible Power Supply UPS HAT For Raspberry Pi Zero, Stable 5V Power Output]
https://www.waveshare.com/ups-hat-c.htm?&aff_id=116255
[240x240, 1.3inch IPS LCD display HAT for Raspberry Pi]
https://www.waveshare.com/1.3inch-lcd-hat.htm?&aff_id=116255
#RaspberryPi #WiFiScanner #CyberSecurity #Python #Scapy #DIYElectronics #RaspberryPiZero2W #HardwareHacking #TechShorts #PassiveSniffing Who is Secretly Talking on Your Wi-Fi Right Now?](https://i.ytimg.com/vi/R3_1hYEB0CI/mqdefault.jpg)

![IMU Motion Tracking UPGRADE: Python Processing with Wireless UDP Stream!
The headache of wired IMU projects is over! In this video, we ditch the slow serial port and upgrade our setup to a fast wireless motion tracking system using an ESP32-S3, Python, and UDP streaming. See how we get immediate data processing and position results right after capture.
Hardware & Software Used:
Microcontroller: ESP32-S3
IMU Sensor: BNO-055 (We explain why we chose it over the built-in QMI8658!)
Programming: Python Script for Data Processing and Visualization
Protocol: UDP Streaming
[Waveshare ESP32-S3 2-inch Dev Board]
https://www.waveshare.com/product/esp32-s3-touch-lcd-2.htm?&aff_id=116255
[BNO055 Adafruit]
https://amzn.to/3XiHRRd
[BNO055 Clone Version]
https://amzn.to/3XZQD6J
[Project Github]
https://github.com/0015/Python-IMU-Data-Sampling-App
#IMU #MotionTracking #WirelessIMU #Python #ESP32 #UDP #ZUPT #DriftCorrection IMU Motion Tracking UPGRADE: Python Processing with Wireless UDP Stream!](https://i.ytimg.com/vi/RUpNuuojN5Q/mqdefault.jpg)


![2025, My first ESP32 device is T5 E-Paper S3 Pro! #ESP32 #LoRa #E-Paper
Product: T5 E-Paper S3 Pro
MCU: ESP32-S3-WROOM-1
Flash / PSRAM: 16M / 8M
Lora: SX1262
Touch: GT911
Driver IC: ED047TC1 (4.7 inches, 960x540 , 16 gray)
Battery Capacity: 1500mAh
Battery Chip: BQ25896, BQ27220
RTC: PCF85063
[T5 E-Paper S3 Pro]
https://lilygo.cc/products/t5-e-paper-s3-pro 2025, My first ESP32 device is T5 E-Paper S3 Pro! #ESP32 #LoRa #E-Paper](https://i.ytimg.com/vi/SFUErz3VVJY/mqdefault.jpg)

![I failed it. I dont recommend the basic model of ESP-Drone. #ESP32
I started this project with high expectations but I failed. Please use this as a reference and I hope your project will be successful.
*When connecting the drone via WiFi from the mobile, the password is 12345678
[ESP-Drone]
https://docs.espressif.com/projects/espressif-esp-drone/en/latest/index.html
[ESP-Drone Sensor Calibration]
https://docs.espressif.com/projects/espressif-esp-drone/en/latest/system.html#sensor-calibration
#ESP32 #Drone #Crazyflie I failed it. I dont recommend the basic model of ESP-Drone. #ESP32](https://i.ytimg.com/vi/SXpK2IH-JWE/mqdefault.jpg)


![[AMB82-Mini IoT AI Camera] *On-Device* object detector powered by Tiny Yolo v7! #standalone
Tiny YOLO version 7 is a simplified version of YOLO version 7, which has a much smaller number of convolution layers than YOLO version 7, which means that Tiny YOLO version 7 does not need to have a large amount of memory and hardware performance but lose some of the detection accuracy.
Lets make one thing clear.
The object detector we are trying to build in a low-power MCU environment is a very simple one. For example, it triggers an action when it detects a person, or acts as a kind of switch to take a certain action. For better performance, it is recommended to run the latest full version of YOLO on a device such as Nano Jetson.
[AMB82-Mini Camera module]
https://amzn.to/4bYOuNk
[Project GitHub]
https://github.com/0015/AMB82-Mini-Board [AMB82-Mini IoT AI Camera] *On-Device* object detector powered by Tiny Yolo v7! #standalone](https://i.ytimg.com/vi/TGqOUVhQQY8/mqdefault.jpg)